Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

It was recently brought to our attention that our paper was missing information regarding when the patient chest computed tomography (CT) scans were obtained and that there were some discrepancies in the clinical metadata, associated with the very large image dataset, that we made publicly available through the China National Center for Bioinformation (http://ncov-ai.big.ac.cn/ download?lang=en). All of the chest CT and clinical metadata used in our prognostic analysis were collected from patients at the time of hospital admission, and we have now added this statement to the STAR Methods section of our paper. We believe that the errors in the clinical metadata were introduced when the chest CT images, clinical metadata, and codes were transferred to the web server, and we have now corrected the errors manually. Although these corrections do not alter any of the conclusions made in the paper, we do apologize for these errors and any confusion that they may have caused.

Since the first description of a coronavirus-related pneumonia outbreak in December 2019, the virus SARS-CoV-2 that causes the infection/disease (COVID-19) has evolved into a pandemic, and as of today, >100 million people globally in over 210 countries have been confirmed to have been infected and two million people have died of COVID-19. This brief review summarized what we have hitherto learned in the following areas: epidemiology, virology, and pathogenesis, diagnosis, use of artificial intelligence in assisting diagnosis, treatment, and vaccine development. As there are a number of parallel developments in each of these areas and some of the development and deployment were at unprecedented speed, we also provided some specific dates for certain development and milestones so that the readers can appreciate the timing of some of these critical events. Of note is the fact that there are diagnostics, antiviral drugs, and vaccines developed and approved by a regulatory within 1 year after the virus was discovered. As a number of developments were conducted in parallel, we also provided the specific dates of a number of critical events so that readers can appreciate the evolution of these research data and our understanding. The world is working together to combat this pandemic. This review also highlights the research and development directions in these areas that will evolve rapidly in the near future.

Highlights d SARS-CoV-2 genome sequencing and phylogenetic analyses identify 35 recurrent mutations d Association with 117 clinical phenotypes reveals potentially important mutations d D500-532 in Nsp1 coding region correlates with lower viral load and serum IFN-b d Viral isolates with D500-532 mutation induce lower IFN-I response in the infected cells

Emerging evidences demonstrate that circular RNAs (circRNAs) are abnormally expressed in tumors and could serve as prognostic markers for cancers. However, the expression patterns and clinical implications of circRNAs in nonsmall cell lung cancer (NSCLC) remain obscure. In this study, we profiled circRNA expressions in 10 pairs of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) after ribosomal RNA-depletion and RNase R digestion to enrich circRNAs. Combining five circRNA computational programs, we found that LUAD and LUSC not only share common expression patterns, but also exhibit distinct circRNA expression signatures. Moreover, the Receiver Operating Characteristic (ROC) curve analysis indicated that hsa_circ_0077837 and hsa_circ_0001821 could serve as potential biomarkers for both LUAD and LUSC, while hsa_circ_0001073 and hsa_circ_0001495 could be diagnostic/ subtyping marker for LUAD and LUSC, respectively. Therefore, our findings highlight the important diagnostic potential of circRNAs in NSCLC.

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BackgroundIn recent years, the pleiotropic roles of vitamin D have been highlighted in various diseases. However, the association between serum vitamin D and COPD is not well studied. This updated systematic review and meta-analysis aimed to assess the relationship between vitamin D and the risk, severity, and exacerbation of COPD.MethodsA systematic literature search was conducted in PubMed, Medline, EMBASE, Chinese National Knowledge Infrastructure, Wanfang, and Weipu databases. The pooled risk estimates were standardized mean difference (SMD) with 95% confidence interval (CI) for vitamin D levels and odds ratio (OR) with 95% CI for vitamin D deficiency. Meta-regression and subgroup analyses were performed on latitude, body mass index, and assay method.ResultsA total of 21 studies, including 4,818 COPD patients and 7,175 controls, were included. Meta-analysis showed that lower serum vitamin D levels were found in COPD patients than in controls (SMD: −0.69, 95% CI: −1.00, −0.38, P<0.001), especially in severe COPD (SMD: −0.87, 95% CI: −1.51, −0.22, P=0.001) and COPD exacerbation (SMD: −0.43, 95% CI: −0.70, −0.15, P=0.002). Vitamin D deficiency was associated with increased risk of COPD (OR: 1.77, 95% CI: 1.18, 2.64, P=0.006) and with COPD severity (OR: 2.83, 95% CI: 2.00, 4.00, P<0.001) but not with COPD exacerbation (OR: 1.17, 95% CI: 0.86, 1.59, P=0.326). Assay methods had significant influence on the heterogeneity of vitamin D deficiency and COPD risk.ConclusionSerum vitamin D levels were inversely associated with COPD risk, severity, and exacerbation. Vitamin D deficiency is associated with increased risk of COPD and severe COPD but not with COPD exacerbation. It is worth considering assay methods in the heterogeneity sources analysis of association between vitamin D deficiency and COPD.

Common lung diseases are first diagnosed via chest X-rays. Here, we show that a fully automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by Coronavirus disease 2019 (COVID-19), assess its severity, and discriminate it from other types of pneumonia. The deep-learning system was developed by using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.88–0.99, between severe and non-severe COVID-19 with an AUC of 0.87, and between severe or non-severe COVID-19 pneumonia and other viral and non-viral pneumonia with AUCs of 0.82–0.98. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists, and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide clinical-decision support.